21.21 Long et al.
developed a size stroop effect paradigm is which the real world size and the
image size may have a different relation among test objects. They had observers
to select one of the two objects with bigger sizes. The object with smaller image
may have a bigger real world size. The RT for the congruent condition is
smaller than the incongruent condition. For most objects, Size classification
accuracy cannot predict identification performance, but stroop effect ER
change. Basic level recognition is not necessary to activate real world size.
21.22 The authors
explored the cortical representation in different areas in the ventral stream
of the object categorical boundaries. They Focused on V1, V2, hV4 and LOC. The
computed the correlation of voxel activations for different objects and used it
as a measurement of distance. The within-category difference decreased while
between-category distance increased from V1 to LOC. The less typical exemplar
tend to have a greater distance from the centroid of the category.
22.24 They use fMRI
adaptation paradigm. The stimuli were colored ring modulated in Red/green or
luminance. The observers were adapted to blank, R/G or ACH ring and tested with
R/G or ACH ring. Vo is identified by contrasting colored and ACH contrast. Without
adaptation, V4,V3V and VO all shows a bias toward color rings. Adapted to R/G
reduced R/G test activation everywhere, but ACH adaptor produced a significant
reduction effect for R/G activation only in VO. The ACH adaptor produced more
effect than R/G on ACH test only in
dorsal areas.
[I am not sure what
is new about this study that we have not known already from previous
literature. ]
22.26 Tyler
demonstrated a new color induction effect in which one can perceive color of
dot inducers on a blank field.
23.4072 The authors
used a block design fMRI to measure BOLD response to images of six individuals
in six expressions. They compute the correlation of voxel responses in FFA, OFA
and STS. They then correlated the fMRI responses with extracted internal and
external images. They found internal features correlated well with expression
and external, identity.
[We can really use
this paradigm for our ORE on facial expression experiment. We were able to
extract various images features. Instead component analysis, we can just
correlate these features with fMRI activation to observe which feature can
predict BOLD activation to ORE.]
26.4005 Using
nominal or subjective equilluminance S-cone modulation colors has no effect on
VEP.
26.4007 In V1, the
BOLD activation for Glaucoma patients for ACH,RG or BY patterns are lower than
normal. However, in LGN, glaucoma patients actually have greater activations.
26.4008 This paper
from Kingdon's lab measured the fusion threshold for dichoptic colors. They
showed that the fusion threshold increased with luminance contrast.
26.4080 The authors
showed a novel Lemon illusion, in which the observers may perceive curvature on
straight lines (thus, shape like a lemon) if the line is bound by curves.
26.4081 The authors suggested that Ebbinghaus
illusion is not due to size contrast. The difference between target and
background size is not sufficient (it is possible to have size contrast without
illusion) nor necessary (one can produced the illusion by a squared contour
surrounding the target).
33.3016 This study
from Webster's lab measured the average color of a patch of random dots in
which the color of a pixel was sample from a distribution along a color axis.
They found that the variability of white setting increased significantly with
any added random variability in the image.
33.3018 The authors
showed that the afterimage for induced color is not complementary colors but
biased toward S-cone contribution.
33.3049 The authors
changed the viewpoint and expression of facial images and measured the
classification of BOLD activations for those images. They found that IT can classify both
viewpoint and expression. However, when there is a joined change in viewpoint and expression.
The expression difference dominate classification.
33.4037 Boynton used
M-sequence to measure fMRI pRF. They found that pRF ignored the scotoma in
stimulus presentation, suggesting a filling-in process is involved.
33.4041 Mulligan used a titration method to calibrate
a display without photometer. The stimulus including stripes of a checkerboard
of two end points to be bisected and a gray. It alternating with gray-upper-end
and gray-lower-end checkerboards. If the gray is too bright, it would move to
an opposite direction form when it is too dark. Thus, we can use minimum motion
as a criterion to determine the gray level that bisect the range.
33.4087 The authors
found that the fMRI activation in PPA and surrounding areas can classify
building of different styles, but not different architects of the same style.
36.3001 The authors
made a review on several aspects of brain responses to symmetry patterns.
36.4001 McCourt et
al. manipulated the luminance of the collinear or the context bars in the White
illusion display. They found that the illusion mainly changed with the
collinear luminance and much less with context luminance.
35.24 This study
from Gallent's lab decomposed images into features (SF, depth, surface
orientation). Send the observers to fMRI scanner and estimated the weighting of
each feature per voxel. Low level features are from converting input images
Gabor wavelets. The depth were from depth map (the test images are rendered
from a 3D model) and the orientation is from surface norms. [CC: this is
clever. Using the rendered images, they completely avoid the tedious task of
image understanding). The local features predicts V1 responses. But the
orientation and depth combined predicts PPA responses. They can then construct
the voxel receptive field, with particular depth and surface orientation
selectivity.
35.28 The authors
compared human behavior data with electrophysiological response to nature
scene. The behavior task is simple 2AFC detection task. The target was Gabor
patch on a patch of a natural scene background. The result is a typical
no-dipper TvC function (for background was a patch of nature scene and thus
provided little excitation to the detector). They then measured the voltage
sensitive dye imaging on monkey brain to the target. The brain area for the
target is used as ROI. They then measured the response to background in target
ROI in each trial and got a histogram of response distribution. They then
computed neurometrics function for various background and estimated threshold
for the target contrast that gave d'=1 for different background contrasts. The
response, when appropriately scheduled, fit human performance data well.
52.13 The authors
use emotional faces and perceptual similarity task (whether two faces had the
same emotion). They showed that there is culture difference between Chinese and
British in perceptual similarity task. There is a difference in categorization.
They then repeated the experiment with lower and higher half faces. Again,
there is no difference between culture. The categorization showed a difference
in the lower face region, not in the upper face regions. They then tried two databases (Chinese and
Caucasian). Again, a own-group advantage in lower face regions.
52.15 The authors
found that there is a low correlation between different holistic face tasks
(inversion, composite and part-whole). Only face inversions correlated with
face perception (0.39). There is no correlation between composite face task and
face perception,
53.4002 The authors
used typical phase tagging paradigm but swap 2D spatial change with depth
change. In this case, they claimed to be able to map depth tuning in cortex.
[Their result seems to be a very preliminary stage. It remains to be seen
whether their method holds].
53.4007 The authors
measured the fMRI bold response to rings of different orientation. They used
MVPC to identify the voxels that contribute most to classification and use
population receptive field to identify the pRF of these voxels. They found that
the ones that contributed most to classification are those tune to the edges of
the rings.
61.13 The authors
uses MEG to measure broadband field response, that is normally acquired with
Ecog. The signal is a broadband increase related to baseline, other than the
stimulus driven response. It is a proxy for mean local spiking activity and is
correlated with fMRI BOLD activation. The MEG field potential is weaker and
more pronounced at high frequency. It is spatially localized by low in SNR.
They used a denoising algorithm. The noise is acquired from PCA of channels
that do not respond to the stimuli (e.g., frontal electrodes in visual
stimulation). In that way, the claim to
be able to measure reliable field potential.
61.14 Following the
previous presentation from the same lab, they compared MEG measured local field
potential and BOLD. They extract several frequency band from MEG signal and
correlated it with BOLD and found individual bands do not correlate with BOLD well,
but broadband field potential.
61.15 They modeled
ganglion cell with normalized response then predict the ganglion responses to
nature images and compute the statistics of the natural images and retinal
signals. The retinal off-neurons dominated low frequency response and there is
an interaction in contrast gain control between P- and M-cells.
63.4066 This paper
from Daniel Baker's lab is also identical to YiChen's thesis. We need to get it
published ASAP.
